Chun-Liang Shih1, Ji-Dung Luo2, John Wen-Cheng Chang3, Tai-Long Chen2, Yu-Tzu Chien4, Chia-Jung Yu5, Chiuan-Chian Chiou6. 1. Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C. 2. Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C. Department of Medical Biotechnology and Laboratory Science, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C. 3. Division of Hematology-Oncology, Department of Internal Medicine, Chang Gung Memorial Hospital, Taoyuan, Taiwan, R.O.C. School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C. 4. Department of Medical Biotechnology and Laboratory Science, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C. 5. Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C. ccchiou@mail.cgu.edu.tw yucj1124@mail.cgu.edu.tw. 6. Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C. Department of Medical Biotechnology and Laboratory Science, College of Medicine, Chang Gung University, Taoyuan, Taiwan, R.O.C. ccchiou@mail.cgu.edu.tw yucj1124@mail.cgu.edu.tw.
Abstract
BACKGROUND: Circulating mRNA is a less invasive and more easily accessed source of samples for biomedical research and clinical applications. However, it is of poor quality. We explored and compared the ability of two high-throughput platforms for the profiling of circulating mRNA regarding their ability to retrieve useful information out of this type of samples. MATERIALS AND METHODS: Circulating mRNAs from three non-small cell lung cancer patients and three healthy controls were analyzed by the cDNA-mediated annealing, selection, extension, and ligation (DASL) assay and high-throughput RNA sequencing (RSEQ). Twelve genes were selected for further confirmation by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: The overall expression profiles derived from the two platforms showed modest-to-moderate correlation. Genes with higher expression levels had higher cross-platform concordance than those of medium- and low-expression levels. In addition, the pathway signatures identified by gene set enrichment analysis from both platforms were in agreement. The RT-q PCR results for the selected genes correlated well with that of RSEQ. CONCLUSION: Genes with higher expression levels have cross-platform concordance and can be potential biomarkers. Furthermore, RSEQ is a better tool for profiling circulating mRNAs. Copyright
BACKGROUND: Circulating mRNA is a less invasive and more easily accessed source of samples for biomedical research and clinical applications. However, it is of poor quality. We explored and compared the ability of two high-throughput platforms for the profiling of circulating mRNA regarding their ability to retrieve useful information out of this type of samples. MATERIALS AND METHODS: Circulating mRNAs from three non-small cell lung cancerpatients and three healthy controls were analyzed by the cDNA-mediated annealing, selection, extension, and ligation (DASL) assay and high-throughput RNA sequencing (RSEQ). Twelve genes were selected for further confirmation by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). RESULTS: The overall expression profiles derived from the two platforms showed modest-to-moderate correlation. Genes with higher expression levels had higher cross-platform concordance than those of medium- and low-expression levels. In addition, the pathway signatures identified by gene set enrichment analysis from both platforms were in agreement. The RT-q PCR results for the selected genes correlated well with that of RSEQ. CONCLUSION: Genes with higher expression levels have cross-platform concordance and can be potential biomarkers. Furthermore, RSEQ is a better tool for profiling circulating mRNAs. Copyright
Authors: Vivian Weiwen Xue; Simon Siu Man Ng; Wing Wa Leung; Brigette Buig Yue Ma; William Chi Shing Cho; Thomas Chi Chuen Au; Allen Chi Shing Yu; Hin Fung Andy Tsang; Sze Chuen Cesar Wong Journal: Front Genet Date: 2018-05-15 Impact factor: 4.599